The course will introduce graduate students to models in population biology. We will build models, analyze them using mathematical and computational methods, and fit them to empirical data using statistical methods such as maximum likelihood and Bayesian inference. Every class will present a scientific problem in population biology, a computational method for tackling it, and a Python implementation of the method. Examples will include models from ecology, evolution, epidemiology, and social behavior. In each case, we will introduce a research question, design a model, choose a method, apply it using the Python program language (a leading programming language for scientific research, data science, and machine learning), analyze and visualize the results, and discuss the conclusions.
Instructor: Yoav Ram
Language: The course will be taught in English.
Environment: The course will be given using interactive Jupyter notebooks with built-in exercises and problems. Students should set up an environment on their computer using Anaconda or use a cloud service like Google Colab.
Prerequisites: Some experience in programming and mathematics. See more details below.